The receptor-like cytoplasmic kinase OsRLCK118 regulates plant development and basal immunity in rice(Oryza sativa L.)

Receptor-like cytoplasmic kinases(RLCKs),which belong to a large subgroup of receptor-like kinases in plants,play crucial roles in plant development and immunity.However,their functions and regulatory mechanisms in plants remain unclear.Here,we report functional characterization of OsRLCK118 from the OsRLCK34 subgroup in rice(Oryza sativa L.).Expression of OsRLCK118 could be induced by infections with Xanthomonas oryzae pv.oryzae(Xoo)strains PXO68 and PXO99.Silencing of OsRLCK118 altered plant height,flag-leaf angle and second-topleaf angle.Silencing of OsRLCK118 also resulted in increasing susceptibility to Xoo and Magnaporthe oryzae(M.oryzae)in rice plants.OsRLCK118 knock-out plants were more sensitive to bacterial blight whereas OsRLCK118 overexpressor plants exhibited increased disease resistance.Expression levels of pathogenesis-related genes of OsPAL1,OsNH1,OsICS1,OsPR1a,OsPR5 and OsPR10 were reduced in the rlck118 mutant compared to wild-type rice(Dongjin)and knock-out of OsRLCK118 compromised the production of reactive oxygen species.These results suggest that OsRLCK118 may modulate basal resistance to Xoo and M.oryzae,possibly through regulation of ROS burst and hormone mediated defense signaling pathway.

A Novel Nucleobase Modification Strategy for Controlling RNA-Guided Nucleic Acid Cleavage

Clustered regularly interspaced short palindromic repeat(CRISPR)technologies have opened new scientific avenues widely used in biomedical research.But simple and efficient strategies to reversibly control CRISPR are lacking.In contrast to previous methods of attaching molecules to the ribose of guide RNAs(gRNAs),we focused on molecules that can directly react with nucleobases.Here,we developed a new strategy to switch off the CRISPR system by efficiently installing 4-(bromomethyl)phenylboronic acid onto nucleobases in gRNAs.CRISPR can then be activated by hydrogen peroxide(H_(2)O_(2)).Collectively,this work demonstrates boronic acid reversibly modulating CRISPR systems through a H_(2)O_(2)-responsive manner.

SpinNet: Spinning convolutional network for lane boundary detection

In this paper,we propose a simple but effective framework for lane boundary detection,called Spin Net.Considering that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive,therefore,analyzing and collecting global context information is crucial for lane boundary detection.To this end,we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global perspective.To extract features in narrow strip-shaped fields,we adopt stripshaped convolutions with kernels which have 1×n or n×1 shape in the spinning convolution layer.To tackle the problem of that straight strip-shaped convolutions are only able to extract features in vertical or horizontal directions,we introduce the concept of feature map rotation to allow the convolutions to be applied in multiple directions so that more information can be collected concerning a whole lane boundary.Moreover,unlike most existing lane boundary detectors,which extract lane boundaries from segmentation masks,our lane boundary parameterization branch predicts a curve expression for the lane boundary for each pixel in the output feature map.And the network utilizes this information to predict the weights of the curve,to better form the final lane boundaries.Our framework is easy to implement and end-to-end trainable.Experiments show that our proposed Spin Net outperforms state-of-the-art methods.

S4Net: Single stage salient-instance segmentation

In this paper, we consider salient instance segmentation. As well as producing bounding boxes,our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also the surrounding context, enabling us to distinguish instances in the same scope even with partial occlusion.Our network is end-to-end trainable and is fast(running at 40 fps for images with resolution 320 × 320). We evaluate our approach on a publicly available benchmark and show that it outperforms alternative solutions. We also provide a thorough analysis of our design choices to help readers better understand the function of each part of our network. Source code can be found at https://github.com/Ruochen Fan/S4 Net.

Robust tracking-by-detection using a selection and completion mechanism

It is challenging to track a target continuously in videos with long-term occlusion,or objects which leave then re-enter a scene.Existing tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online,it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory.The tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior stateof-the-art methods.

A three-stage real-time detector for traffic signs in large panoramas

Traffic sign detection is one of the key components in autonomous driving.Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis.Detecting traffic signs,moving vehicles,and lanes is important for localization and decision making.Traffic signs,especially those that are far from the camera,are small,and so are challenging to traditional object detection methods.In this work,in order to reduce computational cost and improve detection performance,we split the large input images into small blocks and then recognize traffic signs in the blocks using another detection module.Therefore,this paper proposes a three-stage traffic sign detector,which connects a Block Net with an RPN–RCNN detection network.Block Net,which is composed of a set of CNN layers,is capable of performing block-level foreground detection,making inferences in less than 1 ms.Then,the RPN–RCNN two-stage detector is used to identify traffic sign objects in each block;it is trained on a derived dataset named TT100 KPatch.Experiments show that our framework can achieve both state-of-the-art accuracy and recall;its fastest detection speed is 102 fps.